Odds Ratio Case Studies

OGR data source with driver: ESRI Shapefile 
Source: "C:\Users\Leslie\Documents\School\Term 5\HASS - Making Maps I\Project\MakingMaps\sentiment_map\sg-shape", layer: "sg-all"
with 1 features
It has 13 fields

Case 1: Sentiments on Weekdays vs Weekends

t_wday <- tweets %>%
  filter(weekday < 6) # filter only tweets made on weekdays
  
t_wend <- tweets %>%
  filter(weekday > 5) # filter only tweets made on weekends  
  
i_wday <- insta %>%
  filter(weekday < 6) # filter only igs made on weekdays
  
i_wend <- insta %>%
  filter(weekday > 5) # filter only igs made on weekends  

Case 2: Sentiments across various Time Periods

We will be dividing the data into 4 time periods:

• SGT 00:00 - 05:59
• SGT 06:00 - 11:59
  
• SGT 12:00 - 17:59
• SGT 18:00 - 23:59

# Twitter
  
t_time1 <- tweets %>%
  filter(hour(date) < 6) # filter tweets made between 12am - 6am
  
t_time2 <- tweets %>%
  filter(hour(date) >= 6) %>%
  filter(hour(date) < 12) # filter tweets made between 6am - 12pm
  
t_time3 <- tweets %>%
  filter(hour(date) >= 12) %>%
  filter(hour(date) < 18) # filter tweets made between 12pm - 6pm
  
t_time4 <- tweets %>%
  filter(hour(date) >= 18) # filter tweets made between 6pm - 12am
  
# Instagram
  
i_time1 <- insta %>%
  filter(hour(date) < 6) # filter igs made between 12am - 6am
  
i_time2 <- insta %>%
  filter(hour(date) >= 6) %>%
  filter(hour(date) < 12) # filter igs made between 6am - 12pm
  
i_time3 <- insta %>%
  filter(hour(date) >= 12) %>%
  filter(hour(date) < 18) # filter igs made between 12pm - 6pm
  
i_time4 <- insta %>%
  filter(hour(date) >= 18) # filter igs made between 6pm - 12am

<<<<<<< HEAD ======= >>>>>>> parent of 6a02bda... Updated writeup